EmbedMask

EmbedMask: Embedding Coupling for One-stage Instance Segmentation;
Hui Ying, Zhaojin Huang, Shu Liu, Tianjia Shao and Kun Zhou;
arXiv preprint arXiv:1912.01954

[Paper]

Installation

This EmbedMask implementation is based on AdelaiDet, which is also on top of Detectron2.

First install Detectron2 following the official guide: INSTALL.md. Then build AdelaiDet with:

git clone https://github.com/yinghdb/AdelaiDet.git
cd AdelaiDet
python setup.py build develop

Then, follow datasets/README.md to set up the datasets (e.g., MS-COCO).

Pretrained Models

The pretrained models can be downloaded from here. And you should place them in the 'models' directory.

Demo

For demo, run the following command lines:

python demo/demo.py \
    --config-file configs/EmbedMask/MS_R_101_3x.yaml \
    --input demo/images \
    --output demo/outputs \
    --opts MODEL.WEIGHTS models/EmbedMask_R_101_3x.pth

Evaluation

For evaluation on COCO, run:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/EmbedMask/MS_R_50_1x.yaml \
    --eval-only \
    --num-gpus 4 \
    OUTPUT_DIR training_dir/EmbedMask_R_50_1x \
    MODEL.WEIGHTS models/EmbedMask_R_50_1x.pth

Training

For training on COCO, run:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/EmbedMask/MS_R_50_1x.yaml \
    --num-gpus 4 \
    OUTPUT_DIR training_dir/EmbedMask_R_50_1x

Results

Name	box AP (val)	mask AP (val)	box AP (test-dev)	mask AP (test-dev)
EmbedMask_MS_R_50_1x	39.9	36.2	40.1	36.3
EmbedMask_MS_R_101_3x	44.2	39.5	44.6	40.0

Notation

The main network architecture in this implementation is similar with that of CondInst and the auxiliary semantic segmentation task is used to help with the mask prediction.

Citations

Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.

@misc{ying2019embedmask,
    title={EmbedMask: Embedding Coupling for One-stage Instance Segmentation},
    author={Hui Ying and Zhaojin Huang and Shu Liu and Tianjia Shao and Kun Zhou},
    year={2019},
    eprint={1912.01954},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}